Optimization of biodiesel production parameters from Prosopis julifera seed using definitive screening design

The analysis of classical, polynomial regression and cubic spline mathematical models in hemp biodiesel optimization: an experimental comparison

Post-pandemic inflationist pressures, climate changes and extremes, regional conflicts, and soaring food prices caused the food crisis to increase rapidly worldwide. This global problem directs producers and researchers to use oils used as feedstock in biodiesel production effectively. In this context, it is important to assay the transesterification parameters and conduct new optimization studies to increase biodiesel yield. In this study, methyl ester was produced from hemp oil by transesterification using sodium hydroxide (NaOH). Next, classical optimization study was carried out to determine the effects of catalyst amount, alcohol:oil molar ratio, reaction temperature, and reaction time variables on biodiesel yield. Secondly, the cubic spline mathematical model (CSMM) and polynomial regression mathematical model (PRMM) were applied to the first data of this optimization. Among these optimization methods, the utmost biodiesel yield registered was 96.115% at hemp seed oil (HSO):methanol molar ratio of 5.59:1, catalyst concentration of 0.531 wt%, reaction temperature of 42.5 °C, reaction time of 62.1 min, and agitation intensity of 600 rpm at PRMM. Some vital fuel properties obtained from HSO biodiesels as a result of three optimizations satisfied the EN 14214 standard. The results illustrated that the optimal yields from CSMM and PRMM are 0.765% and 1.065% higher, respectively, according to the maximum efficiency obtained from the classical optimization. The outcomes showed that CSMM and PRMM are cost-effective, easy to handle, and promising new approaches.

Optimization of biodiesel production, engine exhaust emissions, and vibration diagnosis using a combined approach of definitive screening design (DSD) and artificial neural network (ANN)

In this study, definitive screening design (DSD) optimization and artificial neural network (ANN) modelling techniques are applied for the production of palm oil biodiesel (POBD). These techniques are implemented to examine the vital contributing factors in achieving maximum POBD yield. For this purpose, seventeen experiments are conducted randomly by varying the four contributing factors. The results of DSD optimization reveal that a biodiesel yield of 96.06% is achieved. Also, the experimental results are trained in ANN for predicting the biodiesel yield. The results proved that the prediction capability of ANN is superior, with a high correlation coefficient (R2) and low mean square error (MSE). Furthermore, the obtained POBD is characterized by significant fuel properties and fatty acid compositions and observed within the standards (ASTM-D675). Finally, the neat POBD is examined for exhaust emissions and engine cylinder vibration analysis. The emissions results confirm a significant drop in NOx (32.46%), HC (40.57%), CO (44.44%), and exhaust smoke (39.65%) compared to diesel fuel at 100% load. Likewise, the engine cylinder vibration measured on top of the cylinder head reveals a low spectral density with low amplitude vibrations observed for POBD at measured loads.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Lignocellulosic Biorefinery Technologies: A Perception into Recent Advances in Biomass Fractionation, Biorefineries, Economic Hurdles and Market Outlook

Lignocellulosic biomasses (LCB) are sustainable and abundantly available feedstocks for the production of biofuel and biochemicals via suitable bioconversion processing. The main aim of this review is to focus on strategies needed for the progression of viable lignocellulosic biomass-based biorefineries (integrated approaches) to generate biofuels and biochemicals. Processing biomass in a sustainable manner is a major challenge that demands the accomplishment of basic requirements relating to cost effectiveness and environmental sustainability. The challenges associated with biomass availability and the bioconversion process have been explained in detail in this review. Limitations associated with biomass structural composition can obstruct the feasibility of biofuel production, especially in mono-process approaches. In such cases, biorefinery approaches and integrated systems certainly lead to improved biofuel conversion. This review paper provides a summary of mono and integrated approaches, their limitations and advantages in LCB bioconversion to biofuel and biochemicals.

Synthesis and characterization of a heterogeneous catalyst from a mixture of waste animal teeth and bone for castor seed oil biodiesel production

The present study focused on the synthesis of heterogeneous catalyst from a mixture of waste animal teeth and bone through thermal method. The produced catalyst was used for castor seed oil (CO) biodiesel production. A different mixing ratio of teeth and bone was used with a calcination temperature range from 650 °C to 1250 °C with 100 °C increment for 3h calcination duration. Thermogravimetric analysis (TGA) for animal teeth and bone was performed to identify the common decomposition temperature range. The effect of calcination temperature on basicity of the catalyst and yield of biodiesel was studied for each teeth and bone mixing ratio. Maximum basicity of 6.12mmol HCl/g and biodiesel of 89.5wt% was obtained by mixing ratio of 25wt% teeth and 75wt% bone at calcination temperature of 1150 °C for 3h. The purity of the produced biodiesel in terms of mono fatty acid methyl esters (FAME) formation was found to be 92.6%. X-ray Diffraction (XRD), X-Ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FT-IR) and TGA was used to characterize the raw and produced catalyst. The maximum yield of FAME (89.5wt% with 92.6% purity) was obtained by 5wt% catalyst loading and 9:1 M ratio of methanol to castor seed oil at 60 °C reaction temperature for 3h. Compositional analysis of the produced CO FAME was performed by FT-IR, gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR). The performance of the produced catalyst was also checked using its reusability for transesterification CO. Further, the physico-chemical properties including rheological properties of the produced CO FAME were characterized by ASTM methods to check its suitability as a liquid biofuel.